Results 181 to 190 of about 507,719 (274)

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Hydraulic support pressure prediction via deep learning with multilevel temporal feature integration. [PDF]

Sci Rep
Yu Q, Sun C, Yang Y, Yang P.
europepmc   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Intelligent diagnosis method for river and lake ecosystem health based on improved slime mold algorithm-optimized SVR. [PDF]

PLoS One
Chi R, Da Y, Li W, Wen D.
europepmc   +1 more source

Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

Advanced Intelligent Discovery, EarlyView.
Multilayer acoustic superscatterers are designed using machine learning to achieve broadband superscattering and strong sound insulation. By incorporating a weighted mean absolute error into the loss function, the forward and inverse neural networks accurately map structural parameters to spectral responses.
Lijuan Fan, Xiangliang Zhang, Ying Wu
wiley   +1 more source

Woa-wtconv-kanformer for long term time series forecasting. [PDF]

PLoS One
Ling Ming M, Wei Min Q, Yi Fan D, Yu Ning Z.   +3 more
europepmc   +1 more source

Screw‐Based Pill for Intelligent Robotic Extraction of Viscous Fluids in Medical Applications

Advanced Intelligent Systems, EarlyView.
Screw‐based pill for intelligent robotic extraction (S‐PIRE) is a magnetically actuated smart capsule with a motorized screw, 3‐axis sensor, and Bluetooth control for site‐specific and minimally invasive remote sampling of viscous fluids, overcoming passive diffusion limitations.
Prima Dewi Sinawang, Victor Garcia‐Gradilla, Fernando Soto, Demir Akin, Margaret Braunreuther, Gerald G. Fuller, Utkan Demirci   +6 more
wiley   +1 more source

Health technology assessment of an automated unit-dose drug distribution system in a high-specialization national referral hospital of the Italian National Health Service. [PDF]

Glob Reg Health Technol Assess
Croce D, De Nardo F, Convenga F, Mueller D, Franzin M.   +4 more
europepmc   +1 more source

A Data‐Centric Approach to Quantifying the Forward and Inverse Relationship Between Laser Powder Bed Fusion Process Parameters and as‐Built Surface Roughness of IN718 Parts

Advanced Intelligent Systems, EarlyView.
This study introduces the first inverse machine learning model to predict laser powder bed fusion process parameters for targeted surface roughness of Inconel 718 parts. Unlike prior approaches, it incorporates spatial surface characteristics for improved accuracy.
Samsul Mahmood, Bart Raeymaekers
wiley   +1 more source

Perceiving Unpredictability for New Energy Power and Electricity Consumption Forecasting. [PDF]

Entropy (Basel)
Zhao L, Dong J, Chen R, Wang Y, Jin Y, Zhao Y.   +5 more
europepmc   +1 more source